{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0b9ffbb2-8d33-eb0c-5a42-0733bdc3b74b",
    "_uuid": "e14f65723f5ac826104e861c45e58525740d8b2a"
   },
   "source": [
    "# Bikeshare数据集上的数据探索\n",
    "\n",
    "casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "2ba3154a-c2aa-3158-1984-63ad2c0c786a",
    "_uuid": "5eb696b95780825e94ddb49787f9fa339fc3833b"
   },
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "21fa35be-878b-b4f2-ef6e-68dc070b8bfa",
    "_uuid": "73aee228226be55c0c8a6e4fcbf818c56cd94926"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n",
    "#print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "be11c1c7-acfe-cb9a-bc81-95d461b884c5",
    "_uuid": "b53750ea6a3a2a02aa4297f5401e29c2f4d92e31"
   },
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据值型特征，用常用统计量观察其分布\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 离散特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n",
      "\n",
      "yr属性的不同取值和出现的次数\n",
      "1    366\n",
      "0    365\n",
      "Name: yr, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday','yr']\n",
    "for col in categorical_features:\n",
    "    print( '\\n%s属性的不同取值和出现的次数'%col)\n",
    "    print (train[col].value_counts())\n",
    "    train[col] = train[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类别型特征的取值不多\n",
    "类别型特征可以采用独热编码（One hot encoding）/哑编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 数值特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002749C9FDC88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002749CBC6D30>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002749CBF46D8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002749CC24198>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对数值型特征，直方图\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "train[numerical_features].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 特征与目标之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 每年骑行量的分布\n",
    "violinplot中用x表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2749ccee908>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['yr',\n",
    "                        'cnt']],\n",
    "              x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011年和2012年的分布差异很大,并且2012年的平均骑行量要高于2011年"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 一年中每天的骑车量\n",
    "用颜色参数hue表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'dayly distribution of counts')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "train['date'] = pd.to_datetime(train['dteday'])\n",
    "train['dayofyear'] = train[\"date\"].dt.dayofyear  #减今年的第几天\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['dayofyear',\n",
    "                           'cnt',\n",
    "                           'yr']],\n",
    "             x='dayofyear',y='cnt',\n",
    "             hue='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每年开始和结束的数量少，中间多，骑行量和季节/月份有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 季节与骑车数量的关系\n",
    "violinplot得到详细分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2749ccb7710>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['season',\n",
    "                        'cnt']],\n",
    "              x=\"season\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "能看出来每个季节骑行量的分布不同\n",
    "barplot利用矩阵条的高度反映数值变量的集中趋势，以及使用errorbar功能（差棒图）来估计变量之间的差值统计。\n",
    "谨记barplot展示的是某种变量分布的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,u'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1453dd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['season',\n",
    "                       'cnt']],\n",
    "           x=\"season\",y=\"cnt\")\n",
    "ax.set(title=\"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "骑行量和季节的关系就很明显了：第2、3、4季度的骑行量明显高于第1季度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 月份与骑车数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,u'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18792290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['mnth',\n",
    "                       'cnt']],\n",
    "           x=\"mnth\",y=\"cnt\")\n",
    "ax.set(title=\"Monthly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 天气和骑车数目的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,u'weathersit distribution of counts')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19add110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['weathersit',\n",
    "                       'cnt']],\n",
    "           x=\"weathersit\",y=\"cnt\")\n",
    "ax.set(title=\"weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6 工作日和节假日的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113f1f790>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113eefe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(ncols=2)\n",
    "sn.barplot(data=train,x='holiday',y='cnt',ax=ax1)\n",
    "sn.barplot(data=train,x='workingday',y='cnt',ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.7 数值型特征和y之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1958ae50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aNEhNmjTRmDFjdOrUKQUEBKhr166qX7++nJycdOzYMd26dUvOzs6aO3euSZ+n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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1452ebd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = train[[\"temp\",\"atemp\",\n",
    "                  \"hum\",\"windspeed\",\n",
    "                  \"casual\",\"registered\",\n",
    "                  \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和温度高度相关\n",
    "目标cnt与温度正相关、湿度和风速负相关"
   ]
  }
 ],
 "metadata": {
  "_change_revision": 0,
  "_is_fork": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
